{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T00:00:20Z","timestamp":1740182420966,"version":"3.37.3"},"reference-count":79,"publisher":"IOP Publishing","issue":"4","license":[{"start":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:00:00Z","timestamp":1728518400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:00:00Z","timestamp":1728518400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100011878","name":"Vlaamse regering","doi-asserted-by":"crossref","award":["Onderzoeksprogramma Artifici\u00eble Intelligentie (AI"],"award-info":[{"award-number":["Onderzoeksprogramma Artifici\u00eble Intelligentie (AI"]}],"id":[{"id":"10.13039\/501100011878","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100010661","name":"Horizon 2020 Framework Programme","doi-asserted-by":"crossref","award":["863476"],"award-info":[{"award-number":["863476"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Mach. Learn.: Sci. Technol."],"published-print":{"date-parts":[[2024,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Tensor networks (TNs) have seen an increase in applications in recent years. While they were originally developed to model many-body quantum systems, their usage has expanded into the field of machine learning. This work adds to the growing range of applications by focusing on planning by combining the generative modeling capabilities of matrix product states and the action selection algorithm provided by active inference. Their ability to deal with the curse of dimensionality, to represent probability distributions, and to dynamically discover hidden variables make matrix product states specifically an interesting choice to use as the generative model in active inference, which relies on \u2018beliefs\u2019 about hidden states within an environment. We evaluate our method on the T-maze and Frozen Lake environments, and show that the TN-based agent acts Bayes optimally as expected under active inference.<\/jats:p>","DOI":"10.1088\/2632-2153\/ad7571","type":"journal-article","created":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T23:01:05Z","timestamp":1724972465000},"page":"045012","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Planning with tensor networks based on active inference"],"prefix":"10.1088","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1967-2195","authenticated-orcid":true,"given":"Samuel T","family":"Wauthier","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tim","family":"Verbelen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bart","family":"Dhoedt","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bram","family":"Vanhecke","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2024,10,10]]},"reference":[{"key":"mlstad7571bib1","doi-asserted-by":"crossref","DOI":"10.2139\/ssrn.4899212","article-title":"Tensor networks for explainable machine learning in cybersecurity","author":"Aizpurua","year":"2024"},{"key":"mlstad7571bib2","doi-asserted-by":"publisher","first-page":"1362","DOI":"10.1038\/s41567-022-01740-7","article-title":"Entanglement spread area law in gapped ground states","volume":"18","author":"Anshu","year":"2022","journal-title":"Nat. Phys."},{"article-title":"An area law and sub-exponential algorithm for 1D systems","year":"2013","author":"Arad","key":"mlstad7571bib3"},{"key":"mlstad7571bib4","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevB.85.195145","article-title":"Improved one-dimensional area law for frustration-free systems","volume":"85","author":"Arad","year":"2012","journal-title":"Phys. Rev. B"},{"key":"mlstad7571bib5","doi-asserted-by":"publisher","first-page":"721","DOI":"10.1038\/nphys2747","article-title":"An area law for entanglement from exponential decay of correlations","volume":"9","author":"Brand\u00e3o","year":"2013","journal-title":"Nat. Phys."},{"article-title":"Openai gym","year":"2016","author":"Brockman","key":"mlstad7571bib6"},{"key":"mlstad7571bib7","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevB.97.085104","article-title":"Equivalence of restricted Boltzmann machines and tensor network states","volume":"97","author":"Chen","year":"2018","journal-title":"Phys. Rev. B"},{"key":"mlstad7571bib8","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevB.99.155131","article-title":"Tree tensor networks for generative modeling","volume":"99","author":"Cheng","year":"2019","journal-title":"Phys. Rev. B"},{"key":"mlstad7571bib9","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevB.103.125117","article-title":"Supervised learning with projected entangled pair states","volume":"103","author":"Cheng","year":"2021","journal-title":"Phys. Rev. B"},{"key":"mlstad7571bib10","doi-asserted-by":"publisher","DOI":"10.1103\/RevModPhys.93.045003","article-title":"Matrix product states and projected entangled pair states: concepts, symmetries, theorems","volume":"93","author":"Cirac","year":"2021","journal-title":"Rev. Mod. Phys."},{"key":"mlstad7571bib11","first-page":"pp 698","article-title":"On the expressive power of deep learning: a tensor analysis","volume":"vol 49","author":"Cohen","year":"2016"},{"key":"mlstad7571bib12","doi-asserted-by":"publisher","first-page":"187","DOI":"10.22331\/q-2019-09-23-187","article-title":"Locally accurate MPS approximations for ground states of one-dimensional gapped local Hamiltonians","volume":"3","author":"Dalzell","year":"2019","journal-title":"Quantum"},{"key":"mlstad7571bib13","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/BF02288367","article-title":"The approximation of one matrix by another of lower rank","volume":"1","author":"Eckart","year":"1936","journal-title":"Psychometrika"},{"key":"mlstad7571bib14","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1007\/BF02099178","article-title":"Finitely correlated states on quantum spin chains","volume":"144","author":"Fannes","year":"1992","journal-title":"Commun. Math. Phys."},{"key":"mlstad7571bib15","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1109\/TMBMC.2023.3272150","article-title":"Control flow in active inference systems\u2014part I: classical and quantum formulations of active inference","volume":"9","author":"Fields","year":"2023","journal-title":"IEEE Trans. Mol. Biol. Multi-Scale Commun."},{"key":"mlstad7571bib16","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1109\/TMBMC.2023.3272158","article-title":"Control flow in active inference systems\u2014part II: tensor networks as general models of control flow","volume":"9","author":"Fields","year":"2023","journal-title":"IEEE Trans. Mol. Biol. Multi-Scale Commun."},{"key":"mlstad7571bib17","doi-asserted-by":"publisher","DOI":"10.1016\/j.biosystems.2021.104513","article-title":"Metabolic limits on classical information processing by biological cells","volume":"209","author":"Fields","year":"2021","journal-title":"Biosystems"},{"author":"Flatorion Institute","key":"mlstad7571bib18"},{"key":"mlstad7571bib19","doi-asserted-by":"publisher","first-page":"713","DOI":"10.1162\/neco_a_01351","article-title":"Sophisticated inference","volume":"33","author":"Friston","year":"2021","journal-title":"Neural Comput."},{"key":"mlstad7571bib20","doi-asserted-by":"publisher","first-page":"862","DOI":"10.1016\/j.neubiorev.2016.06.022","article-title":"Active inference and learning","volume":"68","author":"Friston","year":"2016","journal-title":"Neurosci. Biobehav. Rev."},{"key":"mlstad7571bib21","doi-asserted-by":"publisher","first-page":"2633","DOI":"10.1162\/neco_a_00999","article-title":"Active inference, curiosity and insight","volume":"29","author":"Friston","year":"2017","journal-title":"Neural Comput."},{"article-title":"A tensor network approach to finite markov decision processes","year":"2020","author":"Gillman","key":"mlstad7571bib22"},{"key":"mlstad7571bib23","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.132.197301","article-title":"Combining reinforcement learning and tensor networks, with an application to dynamical large deviations","volume":"132","author":"Gillman","year":"2024","journal-title":"Phys. Rev. Lett."},{"key":"mlstad7571bib24","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevX.8.011006","article-title":"Neural-network quantum states, string-bond states and chiral topological states","volume":"8","author":"Glasser","year":"2018","journal-title":"Phys. Rev. X"},{"key":"mlstad7571bib25","doi-asserted-by":"publisher","first-page":"68169","DOI":"10.1109\/ACCESS.2020.2986279","article-title":"From probabilistic graphical models to generalized tensor networks for supervised learning","volume":"8","author":"Glasser","year":"2020","journal-title":"IEEE Access"},{"key":"mlstad7571bib26","article-title":"Expressive power of tensor-network factorizations for probabilistic modeling","volume":"vol 32","author":"Glasser","year":"2019"},{"key":"mlstad7571bib27","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.98.042114","article-title":"Matrix product operators for sequence-to-sequence learning","volume":"98","author":"Guo","year":"2018","journal-title":"Phys. Rev. E"},{"key":"mlstad7571bib28","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevB.88.075133","article-title":"Post-matrix product state methods: to tangent space and beyond","volume":"88","author":"Haegeman","year":"2013","journal-title":"Phys. Rev. B"},{"key":"mlstad7571bib29","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevX.8.031012","article-title":"Unsupervised generative modeling using matrix product states","volume":"8","author":"Han","year":"2018","journal-title":"Phys. Rev. X"},{"key":"mlstad7571bib30","doi-asserted-by":"publisher","DOI":"10.1088\/1742-5468\/2007\/08\/P08024","article-title":"An area law for one-dimensional quantum systems","author":"Hastings","year":"2007","journal-title":"J. Stat. Mech."},{"key":"mlstad7571bib31","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1007\/s00220-006-1535-6","article-title":"Aspects of generic entanglement","volume":"265","author":"Hayden","year":"2006","journal-title":"Commun. Math. Phys."},{"key":"mlstad7571bib32","doi-asserted-by":"publisher","first-page":"4098","DOI":"10.21105\/joss.04098","article-title":"pymdp: a python library for active inference in discrete state spaces","volume":"7","author":"Heins","year":"2022","journal-title":"J. Open Source Softw."},{"key":"mlstad7571bib33","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.pneurobio.2012.05.003","article-title":"Waking and dreaming consciousness: neurobiological and functional considerations","volume":"98","author":"Hobson","year":"2012","journal-title":"Prog. Neurobiol."},{"article-title":"A tensor network implementation of multi agent reinforcement learning","year":"2024","author":"Howard","key":"mlstad7571bib34"},{"key":"mlstad7571bib35","doi-asserted-by":"crossref","DOI":"10.1016\/j.acha.2023.101575","article-title":"Generative modeling via tensor train sketching","author":"Hur","year":"2023"},{"key":"mlstad7571bib36","doi-asserted-by":"publisher","DOI":"10.1088\/2399-6528\/ac94be","article-title":"Experimental indications of non-classical brain functions","volume":"6","author":"Kerskens","year":"2022","journal-title":"J. Phys. Commun."},{"article-title":"Expressive power of recurrent neural networks","year":"2018","author":"Khrulkov","key":"mlstad7571bib37"},{"key":"mlstad7571bib38","doi-asserted-by":"publisher","first-page":"L955","DOI":"10.1088\/0305-4470\/24\/16\/012","article-title":"Equivalence and solution of anisotropic spin-1 models and generalized t-j fermion models in one dimension","volume":"24","author":"Klumper","year":"1991","journal-title":"J. Phys. A: Math. Gen."},{"key":"mlstad7571bib39","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1209\/0295-5075\/24\/4\/010","article-title":"Matrix product ground states for one-dimensional spin-1 quantum antiferromagnets","volume":"24","author":"Kl\u00fcmper","year":"1993","journal-title":"Europhys. Lett."},{"year":"1998","author":"LeCun","key":"mlstad7571bib40"},{"key":"mlstad7571bib41","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1038\/nn.4479","article-title":"REM sleep selectively prunes and maintains new synapses in development and learning","volume":"20","author":"Li","year":"2017","journal-title":"Nat. Neurosci."},{"key":"mlstad7571bib42","doi-asserted-by":"publisher","DOI":"10.1088\/1367-2630\/ab31ef","article-title":"Machine learning by unitary tensor network of hierarchical tree structure","volume":"21","author":"Liu","year":"2019","journal-title":"New J. Phys."},{"key":"mlstad7571bib43","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.107.L012103","article-title":"Tensor networks for unsupervised machine learning","volume":"107","author":"Liu","year":"2023","journal-title":"Phys. Rev. E"},{"article-title":"Quantum tensor networks for variational reinforcement learning","year":"2020","author":"Liu","key":"mlstad7571bib44"},{"key":"mlstad7571bib45","doi-asserted-by":"publisher","first-page":"1058","DOI":"10.1038\/s42256-023-00732-3","article-title":"Many-body control with reinforcement learning and tensor networks","volume":"5","author":"Lu","year":"2023","journal-title":"Nat. Mach. Intell."},{"key":"mlstad7571bib46","first-page":"pp 7301","article-title":"Tesseract: tensorised actors for multi-agent reinforcement learning","volume":"vol 139","author":"Mahajan","year":"2021"},{"key":"mlstad7571bib47","first-page":"pp 74","article-title":"Schr\u00f6dingerrnn: generative modeling of raw audio as a continuously observed quantum state","volume":"vol 107","author":"Mencia Uranga","year":"2020"},{"key":"mlstad7571bib48","doi-asserted-by":"publisher","first-page":"780","DOI":"10.1038\/s42256-023-00687-5","article-title":"Self-correcting quantum many-body control using reinforcement learning with tensor networks","volume":"5","author":"Metz","year":"2023","journal-title":"Nat. Mach. Intell."},{"key":"mlstad7571bib49","first-page":"pp 3079","article-title":"Tensor networks for probabilistic sequence modeling","volume":"vol 130","author":"Miller","year":"2021"},{"key":"mlstad7571bib50","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevB.82.205105","article-title":"Simulating strongly correlated quantum systems with tree tensor networks","volume":"82","author":"Murg","year":"2010","journal-title":"Phys. Rev. B"},{"article-title":"Dynamic programming","year":"2020","author":"Ng","key":"mlstad7571bib51"},{"key":"mlstad7571bib52","doi-asserted-by":"publisher","first-page":"538","DOI":"10.1038\/s42254-019-0086-7","article-title":"Tensor networks for complex quantum systems","volume":"1","author":"Or\u00fas","year":"2019","journal-title":"Nat. Rev. Phys."},{"key":"mlstad7571bib53","doi-asserted-by":"publisher","first-page":"2295","DOI":"10.1137\/090752286","article-title":"Tensor-train decomposition","volume":"33","author":"Oseledets","year":"2011","journal-title":"SIAM J. Sci. Comput."},{"year":"2022","author":"Parr","key":"mlstad7571bib54"},{"article-title":"Generative modeling via hierarchical tensor sketching","year":"2023","author":"Peng","key":"mlstad7571bib55"},{"key":"mlstad7571bib56","doi-asserted-by":"publisher","first-page":"401","DOI":"10.26421\/QIC7.5-6-1","article-title":"Matrix product state representations","volume":"7","author":"Perez-Garcia","year":"2007","journal-title":"Quantum Inf. Comput."},{"key":"mlstad7571bib57","doi-asserted-by":"publisher","first-page":"0061","DOI":"10.34133\/icomputing.0061","article-title":"Tensor networks for interpretable and efficient quantum-inspired machine learning","volume":"2","author":"Ran","year":"2023","journal-title":"Intell. Comput."},{"key":"mlstad7571bib58","doi-asserted-by":"publisher","DOI":"10.1088\/1757-899X\/1261\/1\/012020","article-title":"Active inference, preference learning and adaptive behaviour","volume":"1261","author":"Sajid","year":"2022","journal-title":"IOP Conf. Ser.: Mater. Sci. Eng."},{"year":"1944","author":"Schr\u00f6dinger","key":"mlstad7571bib59"},{"key":"mlstad7571bib60","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.100.030504","article-title":"Entropy scaling and simulability by matrix product states","volume":"100","author":"Schuch","year":"2008","journal-title":"Phys. Rev. Lett."},{"key":"mlstad7571bib61","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevB.106.205136","article-title":"Neural tensor contractions and the expressive power of deep neural quantum states","volume":"106","author":"Sharir","year":"2022","journal-title":"Phys. Rev. B"},{"key":"mlstad7571bib62","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevA.74.022320","article-title":"Classical simulation of quantum many-body systems with a tree tensor network","volume":"74","author":"Shi","year":"2006","journal-title":"Phys. Rev. A"},{"key":"mlstad7571bib63","first-page":"pp 1979","article-title":"Learning hidden quantum markov models","volume":"vol 84","author":"Srinivasan","year":"2018"},{"key":"mlstad7571bib64","doi-asserted-by":"publisher","first-page":"1236","DOI":"10.3390\/e21121236","article-title":"Probabilistic modeling with matrix product states","volume":"21","author":"Stokes","year":"2019","journal-title":"Entropy"},{"key":"mlstad7571bib65","article-title":"Supervised learning with tensor networks","volume":"vol 29","author":"Stoudenmire","year":"2016"},{"key":"mlstad7571bib66","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevB.101.075135","article-title":"Generative tensor network classification model for supervised machine learning","volume":"101","author":"Sun","year":"2020","journal-title":"Phys. Rev. B"},{"key":"mlstad7571bib67","doi-asserted-by":"publisher","DOI":"10.1088\/1367-2630\/ac6232","article-title":"Explainable natural language processing with matrix product states","volume":"24","author":"Tangpanitanon","year":"2022","journal-title":"New J. Phys."},{"key":"mlstad7571bib68","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevB.99.165121","article-title":"Simulating excitation spectra with projected entangled-pair states","volume":"99","author":"Vanderstraeten","year":"2019","journal-title":"Phys. Rev. B"},{"key":"mlstad7571bib69","doi-asserted-by":"publisher","first-page":"7","DOI":"10.21468\/SciPostPhysLectNotes.7","article-title":"Tangent-space methods for uniform matrix product states","author":"Vanderstraeten","year":"2019","journal-title":"SciPost Phys. Lect. Notes"},{"article-title":"Renormalization algorithms for quantum-many body systems in two and higher dimensions","year":"2004","author":"Verstraete","key":"mlstad7571bib70"},{"key":"mlstad7571bib71","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.99.220405","article-title":"Entanglement renormalization","volume":"99","author":"Vidal","year":"2007","journal-title":"Phys. Rev. Lett."},{"article-title":"Generative modeling with projected entangled-pair states","year":"2022","author":"Vieijra","key":"mlstad7571bib72"},{"article-title":"Tensor networks meet neural networks: a survey and future perspectives","year":"2023","author":"Wang","key":"mlstad7571bib73"},{"key":"mlstad7571bib74","first-page":"pp 285","article-title":"Learning generative models for active inference using tensor networks","author":"Wauthier","year":"2023"},{"key":"mlstad7571bib75","doi-asserted-by":"publisher","first-page":"2863","DOI":"10.1103\/PhysRevLett.69.2863","article-title":"Density matrix formulation for quantum renormalization groups","volume":"69","author":"White","year":"1992","journal-title":"Phys. Rev. Lett."},{"key":"mlstad7571bib76","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevB.72.180403","article-title":"Density matrix renormalization group algorithms with a single center site","volume":"72","author":"White","year":"2005","journal-title":"Phys. Rev. B"},{"key":"mlstad7571bib77","doi-asserted-by":"publisher","first-page":"3844","DOI":"10.1103\/PhysRevB.48.3844","article-title":"Numerical renormalization-group study of low-lying eigenstates of the antiferromagnetic s = 1 Heisenberg chain","volume":"48","author":"White","year":"1993","journal-title":"Phys. Rev. B"},{"key":"mlstad7571bib78","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevB.97.235155","article-title":"Topological nature of spinons and holons: elementary excitations from matrix product states with conserved symmetries","volume":"97","author":"Zauner-Stauber","year":"2018","journal-title":"Phys. Rev. B"},{"author":"Mel Tillery","key":"mlstad7571bib79"}],"container-title":["Machine Learning: Science and Technology"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad7571","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad7571\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad7571","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad7571\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad7571\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad7571\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad7571\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad7571\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T06:37:48Z","timestamp":1728542268000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad7571"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,10]]},"references-count":79,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,10,10]]},"published-print":{"date-parts":[[2024,12,1]]}},"URL":"https:\/\/doi.org\/10.1088\/2632-2153\/ad7571","relation":{},"ISSN":["2632-2153"],"issn-type":[{"type":"electronic","value":"2632-2153"}],"subject":[],"published":{"date-parts":[[2024,10,10]]},"assertion":[{"value":"Planning with tensor networks based on active inference","name":"article_title","label":"Article Title"},{"value":"Machine Learning: Science and Technology","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2024 The Author(s). Published by IOP Publishing Ltd","name":"copyright_information","label":"Copyright Information"},{"value":"2024-03-06","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2024-08-29","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2024-10-10","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}